I am a joint postdoc at UC Berkeley & Caltech, working with Prof. Jennifer Chayes, Prof. Christian Borgs, and Prof. Anima Anandkumar. Meanwhile, I have close collaboration with Prof. Hongyu Guo from NRC.

I got my CS Ph.D. degree with at Quebec Artificial Intelligence Institute (AKA Mila) and Université de Montréal, advised by Prof. Jian Tang.

I got my CS master’s degree from University of Wisconsin-Madison, and was the graduate researcher at Morgridge Institute for Research. During my stay in UW-Madison, I started my first research project and was fortunately advised by Prof. Anthony Gitter, Prof. Yingyu Liang, and Prof. Dimitris Papailiopoulos. Proir to that, I got my bachelor’s degree from Shandong University.

E-Mail: shengchao1224 at gmail dot com

I also want to share some inspiring research values (special thanks to Weiyang)


Selected Publications

  • NucleusDiff: Manifold-Constrained Nucleus-Level Denoising Diffusion Model for Structure-Based Drug Design
    Shengchao Liu*, Divin Yan*, Weitao Du, Hongyu Guo, Christian Borgs*, Jennifer Chayes*, Anima Anandkumar*
  • ChatDrug: ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback
    Shengchao Liu*, Jiongxiao Wang*, Yijin Yang, Chengpeng Wang, Ling Liu, Hongyu Guo, Chaowei Xiao
    ICLR 2024
    [Paper] [Arxiv] [Project Page] [Code]
    [ICML SynS and ML Workshop 2023 Oral]
  • ChatPathway: Conversational Large Language Models for Biology Pathway Detection
    Yanjing Li, Hannan Xu, Haiteng Zhao, Hongyu Guo, Shengchao Liu
    [Arxiv]
    [NeurIPS GLFrontiers Workshop 2023 Oral]
  • ProteinDT: A Text-guided Protein Design Framework
    Shengchao Liu, Yanjing Li, Zhuoxinran Li, Anthony Gitter, Yutao Zhu, Jiarui Lu, Zhao Xu, Weili Nie, Arvind Ramanathan, Chaowei Xiao*, Jian Tang*, Hongyu Guo*, Anima Anandkumar*
    [Arxiv] [Project Page] [Code]
  • MoleculeSTM: Multi-modal Molecule Structure-text Model for Text-based Editing and Retrieval
    Shengchao Liu, Weili Nie, Chengpeng Wang, Jiarui Lu, Zhuoran Qiao, Ling Liu, Jian Tang, Chaowei Xiao, Anima Anandkumar
    Nature Machine Intelligence 2023
    [Paper] [Arxiv] [Project Page] [Code]
    [NeurIPS AI4Science Workshop 2022]
  • GraphCG: Unsupervised Discovery of Steerable Factors When Graph Deep Generative Models Are Entangled
    Shengchao Liu, Chengpeng Wang, Jiarui Lu, Weili Nie, Hanchen Wang, Zuoxinran Li, Bolei Zhou, Jian Tang
    TMLR 2024
    [Paper] [Arxiv] [Project Page] [Code]
    [NeurIPS GLFrontiers Workshop 2022 Oral]
  • CrystalFlow: Equivariant Flow Matching Framework for Learning Molecular Cluster Crystallization
    Shengchao Liu, Divin Yan, Hongyu Guo*, Anima Anandkumar*
  • NeuralMD: A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics
    Shengchao Liu*, Weitao Du*, Yanjing Li, Zhuoxinran Li, Vignesh Bhethanabotla, Nakul Rampal, Omar Yaghi, Christian Borgs, Anima Anandkumar*, Hongyu Guo*, Jennifer Chayes*
    [Arxiv] [Project Page] [Code]
    [ICLR AI4DifferentialEquations Workshop 2024 Oral]
  • MoleculeSDE: A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining
    Shengchao Liu*, Weitao Du*, Zhiming Ma, Hongyu Guo, Jian Tang
    ICML 2023
    [Paper] [Arxiv] [Project Page] [Code]
  • Bad Global Minima Exist and SGD Can Reach Them
    Shengchao Liu, Dimitris Papailiopoulos, Dimitris Achlioptas
    NeurIPS 2020
    [Paper] [Code] [Video, NeurIPS 2020]
    [ICML Deep Learning Phenomena Workshop 2019 Oral]
  • MoleculeJAE: Molecule Joint Auto-Encoding: Trajectory Pretraining with 2D and 3D Diffusion
    Weitao Du, Jiujiu Chen, Xuecang Zhang, Zhiming Ma, Shengchao Liu
    NeurIPS 2023
    [Paper] [Arxiv] [Code]
  • MoleculeSDE: A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining
    Shengchao Liu*, Weitao Du*, Zhiming Ma, Hongyu Guo, Jian Tang
    ICML 2023
    [Paper] [Arxiv] [Project Page] [Code]
  • GeoSSL: Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching
    Shengchao Liu, Hongyu Guo, Jian Tang
    ICLR 2023
    [Paper] [Arxiv] [Project Page] [Code]
  • GraphMVP: Pre-training Molecular Graph Representation with 3D Geometry
    Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, Jian Tang
    ICLR 2022
    [Paper] [Arxiv] [Project Page] [Code]
    [NeurIPS SSL Workshop 2021]
    [ICLR GTRL Workshop 2022 Spotlight]
  • SGNN-EBM: Structured Multi-task Learning for Molecular Property Prediction
    Shengchao Liu, Meng Qu, Zuobai Zhang, Huiyu Cai, Jian Tang
    AISTATS 2022
    [Paper] [Arxiv] [Project Page] [Code]
    [NeurIPS AI4Science Workshop 2021]
  • LBTW: Loss-Balanced Task Weighting to Reduce Negative Transfer in Multi-Task Learning
    Shengchao Liu, Yingyu Liang, Anthony Gitter
    AAAI-Student Abstract 2019
    [Paper] [Appendix] [Code]
  • Geom3D: Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials
    Shengchao Liu, Weitao Du, Yanjing Li, Zhuoxinran Li, Zhiling Zheng, Chenru Duan, Zhiming Ma, Omar Yaghi, Anima Anandkumar, Christian Borgs, Jennifer Chayes, Hongyu Guo, Jian Tang
    NeurIPS Datasets and Benchmarks 2023
    [Paper] [Arxiv] [Code]
  • GraphMVP: Pre-training Molecular Graph Representation with 3D Geometry
    Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, Jian Tang
    ICLR 2022
    [Paper] [Arxiv] [Project Page] [Code]
    [NeurIPS SSL Workshop 2021]
    [ICLR GTRL Workshop 2022 Spotlight]
  • AWARE: Attentive Walk-Aggregating Graph Neural Networks
    Mehmet F. Demirel, Shengchao Liu, Siddhant Garg, Zhenmei Shi, Yingyu Liang
    TMLR 2022
    [Paper] [Arxiv] [Code]
  • N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
    Shengchao Liu, Mehmet Furkan Demirel, Yingyu Liang
    NeurIPS 2019 Spotlight
    [Paper] [Arxiv] [Code]
    [NeurIPS MLMM Workshop 2018]